par 1 Introduction to Generative AI
1.1 Define Generative AI
1.2 The Essence of Generative AI
1.3 Key Components of AI
1.4 Exploring Generative AI Domains
1.4.1 Text Generation
1.4.2 Image Generation
1.4.3 Audio Generation
1.4.4 Video Generation
1.5 Leading Innovators and Their Contributions
1.6 How Does Generative AI Work?
1.7 Generative AI Interfaces
1.8 Recent Transformative Developments in Generative AI
1.9 Current Capabilities and Applications of Generative AI
1.10 Impact on Various Industries
1.11 Comprehensive Overview of Generative AI Applications
1.12 Pros and Cons of Generative AI
1.12.1 Pros
1.12.2 Cons
1.13 Addressing Ethical Considerations
1.14 Summary
1.15 Multiple-choice Questions
1.16 Answers
References
2 Evolution of Neural Networks to Large Language Models
2.1 Natural Language Processing
2.2 Probabilistic Models
2.3 N-Gram Models
2.4 Hidden Markov Models (HMMs)
2.5 Neural Network-Based Language Models
2.6 Recurrent Neural Networks (RNNs)
2.6.1 Understanding RNNs and Sequential Data
2.6.2 RNN Architecture
2.6.3 Handling Variable-Length Sequences
2.6.4 Temporal Dependencies and “Memory”
2.6.5 Challenges with RNNs
2.6.6 Limitations of RNNs
2.7 Long Short-Term Memory (LSTM) Networks
2.7.1 Key Components of LSTM Networks
2.7.2 LSTM Equations
2.7.3 Applications of LSTM Networks
2.7.4 Advantages of LSTM Networks
2.7.5 Limitations
2.8 Gated Recurrent Unit (GRU) Networks
2.8.1 Key Components of GRU Networks
2.8.2 GRU Equations
2.8.3 How GRUs Work?
2.8.4 Advantages of GRU Networks
2.8.5 Applications of GRU Networks
2.8.6 GRU Versus LSTM
2.8.7 Limitations of GRU Networks
2.9 Encoder–Decoder Networks
2.9.1 Components of the Encoder–Decoder Architecture
2.9.2 How the Encoder–Decoder Architecture Works?
2.9.3 Key Challenges and Improvements
2.9.4 Applications of the Encoder–Decoder Architecture
2.9.5 Advantages of the Encoder–Decoder Architecture
2.9.6 Limitations
2.9.7 Enhancements and Variations
2.10 Attention Mechanism
2.10.1 Traditional Encoder–Decoder Limitation
2.10.2 Introduction of the Attention Mechanism
2.10.3 How the Attention Mechanism Works?
2.10.4 Visualization of Attention
2.10.5 Applications and Impact
2.10.6 Variations and Extensions
2.10.7 Advantages of the Attention Mechanism
2.10.8 Limitations..
